checkpoint

.gemini/agents/tier1-orchestrator.md

@@ -0,0 +1,18 @@
+---
+name: tier1-orchestrator
+description: Tier 1 Orchestrator for product alignment and high-level planning.
+model: gemini-3.1-pro-preview
+tools:
+  - read_file
+  - list_directory
+  - glob
+  - grep_search
+  - google_web_search
+  - web_fetch
+  - codebase_investigator
+  - activate_skill
+  - discovered_tool_run_powershell
+---
+STRICT SYSTEM DIRECTIVE: You are a Tier 1 Orchestrator.
+Focused on product alignment, high-level planning, and track initialization.
+ONLY output the requested text. No pleasantries.

.gemini/agents/tier2-tech-lead.md

@@ -0,0 +1,20 @@
+---
+name: tier2-tech-lead
+description: Tier 2 Tech Lead for architectural design and execution.
+model: gemini-3-flash-preview
+tools:
+  - read_file
+  - write_file
+  - replace
+  - list_directory
+  - glob
+  - grep_search
+  - google_web_search
+  - web_fetch
+  - codebase_investigator
+  - activate_skill
+  - discovered_tool_run_powershell
+---
+STRICT SYSTEM DIRECTIVE: You are a Tier 2 Tech Lead.
+Focused on architectural design and track execution.
+ONLY output the requested text. No pleasantries.

.gemini/agents/tier3-worker.md

@@ -0,0 +1,22 @@
+---
+name: tier3-worker
+description: Stateless Tier 3 Worker for code implementation and TDD.
+model: gemini-3-flash-preview
+tools:
+  - read_file
+  - write_file
+  - replace
+  - list_directory
+  - glob
+  - grep_search
+  - google_web_search
+  - web_fetch
+  - codebase_investigator
+  - activate_skill
+  - discovered_tool_run_powershell
+---
+STRICT SYSTEM DIRECTIVE: You are a stateless Tier 3 Worker (Contributor).
+Your goal is to implement specific code changes or tests based on the provided task.
+You have access to tools for reading and writing files, codebase investigation, and web tools.
+You CAN execute PowerShell scripts or run shell commands via discovered_tool_run_powershell for verification and testing.
+Follow TDD and return success status or code changes. No pleasantries, no conversational filler.

.gemini/agents/tier4-qa.md

@@ -0,0 +1,20 @@
+---
+name: tier4-qa
+description: Stateless Tier 4 QA Agent for log analysis and diagnostics.
+model: gemini-2.5-flash-lite
+tools:
+  - read_file
+  - list_directory
+  - glob
+  - grep_search
+  - google_web_search
+  - web_fetch
+  - codebase_investigator
+  - activate_skill
+  - discovered_tool_run_powershell
+---
+STRICT SYSTEM DIRECTIVE: You are a stateless Tier 4 QA Agent.
+Your goal is to analyze errors, summarize logs, or verify tests.
+You have access to tools for reading files, exploring the codebase, and web tools.
+You CAN execute PowerShell scripts or run shell commands via discovered_tool_run_powershell for diagnostics.
+ONLY output the requested analysis. No pleasantries.

.gemini/policies/mma_headless.toml

@@ -0,0 +1,22 @@
+[[rule]]
+toolName = [
+  "read_file",
+  "write_file",
+  "replace",
+  "list_directory",
+  "glob",
+  "grep_search",
+  "search_files",
+  "get_file_summary",
+  "google_web_search",
+  "web_fetch",
+  "codebase_investigator",
+  "cli_help",
+  "activate_skill",
+  "run_shell_command",
+  "run_powershell",
+  "discovered_tool_run_powershell"
+]
+decision = "allow"
+priority = 900
+description = "Allow all MMA tools for sub-agents in headless mode."

.gemini/settings.json

@@ -1,4 +1,7 @@
 {
+  "experimental": {
+    "enableAgents": true
+  },
   "tools": {
     "discoveryCommand": "python C:/projects/manual_slop/scripts/tool_discovery.py",
     "whitelist": [

conductor/archive/mma_data_architecture_dag_engine/plan.md

@@ -0,0 +1,19 @@
+# Implementation Plan: MMA Data Architecture & DAG Engine
+
+## Phase 1: Track-Scoped State Management
+- [x] Task: Define the data schema for a Track (Metadata, Discussion History, Task List). [2efe80e]
+- [x] Task: Update `project_manager.py` to create and read from `tracks/<track_id>/state.toml`. [e1a3712]
+- [x] Task: Ensure Tier 2 (Tech Lead) history is securely scoped to the active track's state file. [b845b89]
+
+## Phase 2: Python DAG Engine
+- [x] Task: Create a `Task` class with `status` (Blocked, Ready, In Progress, Review, Done) and `depends_on` fields. [a3cfeff]
+- [x] Task: Implement a topological sorting algorithm to resolve execution order. [f85ec9d]
+- [x] Task: Write robust unit tests verifying cycle detection and dependency resolution. [f85ec9d]
+
+## Phase 3: Execution State Machine
+- [x] Task: Implement the core loop that evaluates the DAG and identifies "Ready" tasks. [154957f]
+- [x] Task: Create configuration settings for "Auto-Queue" vs "Manual Step" execution modes. [154957f]
+- [x] Task: Connect the state machine to the backend dispatcher, preparing it for GUI integration. [2429b7c]
+
+## Phase: Review Fixes
+- [x] Task: Apply review suggestions [6548ce6]

conductor/product.md

@@ -15,7 +15,10 @@ To serve as an expert-level utility for personal developer use on small projects
   - **Tier 2 (Tech Lead):** Technical oversight and track execution (`/conductor:implement`) using `gemini-2.5-flash`. Maintains persistent context throughout implementation.
   - **Tier 3 (Worker):** Surgical code implementation and TDD using `gemini-2.5-flash` or `deepseek-v3`. Operates statelessly with tool access and dependency skeletons.
   - **Tier 4 (QA):** Error analysis and diagnostics using `gemini-2.5-flash` or `deepseek-v3`. Operates statelessly with tool access.
-  - **MMA Delegation Engine:** Utilizes the `mma-exec` CLI and `mma.ps1` helper to route tasks, ensuring role-scoped context and detailed observability via timestamped sub-agent logs. Supports dynamic ticket creation and dependency resolution via an automated Dispatcher Loop.
+  - **MMA Delegation Engine:** Route tasks, ensuring role-scoped context and detailed observability via timestamped sub-agent logs. Supports dynamic ticket creation and dependency resolution via an automated Dispatcher Loop.
+  - **Track-Scoped State Management:** Segregates discussion history and task progress into per-track state files (e.g., `conductor/tracks/<track_id>/state.toml`). This prevents global context pollution and ensures the Tech Lead session is isolated to the specific track's objective.
+  - **Native DAG Execution Engine:** Employs a Python-based Directed Acyclic Graph (DAG) engine to manage complex task dependencies, supporting automated topological sorting and robust cycle detection.
+  - **Programmable Execution State Machine:** Governing the transition between "Auto-Queue" (autonomous worker spawning) and "Step Mode" (explicit manual approval for each task transition).
   - **Role-Scoped Documentation:** Automated mapping of foundational documents to specific tiers to prevent token bloat and maintain high-signal context.
 - **Strict Memory Siloing:** Employs tree-sitter AST-based interface extraction (Skeleton View, Curated View) and "Context Amnesia" to provide workers only with the absolute minimum context required, preventing hallucination loops.
 - **Explicit Execution Control:** All AI-generated PowerShell scripts require explicit human confirmation via interactive UI dialogs before execution, supported by a global "Linear Execution Clutch" for deterministic debugging.

conductor/tech-stack.md

@@ -37,6 +37,7 @@
 - **pytest:** For unit and integration testing, leveraging custom fixtures for live GUI verification.
 - **ApiHookClient:** A dedicated IPC client for automated GUI interaction and state inspection.
 - **mma-exec / mma.ps1:** Python-based execution engine and PowerShell wrapper for managing the 4-Tier MMA hierarchy and automated documentation mapping.
+- **dag_engine.py:** A native Python utility implementing `TrackDAG` and `ExecutionEngine` for dependency resolution, cycle detection, and programmable task execution loops.
 
 ## Architectural Patterns
 

conductor/tracks.md

@@ -20,12 +20,7 @@ This file tracks all major tracks for the project. Each track has its own detail
 
 ---
 
-- [ ] **Track: MMA Data Architecture & DAG Engine**
+- [~] **Track: Tiered Context Scoping & HITL Approval**
-*Link: [./tracks/mma_data_architecture_dag_engine/](./tracks/mma_data_architecture_dag_engine/)*
-
----
-
-- [ ] **Track: Tiered Context Scoping & HITL Approval**
 *Link: [./tracks/tiered_context_scoping_hitl_approval/](./tracks/tiered_context_scoping_hitl_approval/)*
 
 ---

conductor/tracks/mma_data_architecture_dag_engine/plan.md

@@ -1,16 +0,0 @@
-# Implementation Plan: MMA Data Architecture & DAG Engine
-
-## Phase 1: Track-Scoped State Management
-- [x] Task: Define the data schema for a Track (Metadata, Discussion History, Task List). [2efe80e]
-- [ ] Task: Update `project_manager.py` to create and read from `tracks/<track_id>/state.toml`.
-- [ ] Task: Ensure Tier 2 (Tech Lead) history is securely scoped to the active track's state file.
-
-## Phase 2: Python DAG Engine
-- [ ] Task: Create a `Task` class with `status` (Blocked, Ready, In Progress, Review, Done) and `depends_on` fields.
-- [ ] Task: Implement a topological sorting algorithm to resolve execution order.
-- [ ] Task: Write robust unit tests verifying cycle detection and dependency resolution.
-
-## Phase 3: Execution State Machine
-- [ ] Task: Implement the core loop that evaluates the DAG and identifies "Ready" tasks.
-- [ ] Task: Create configuration settings for "Auto-Queue" vs "Manual Step" execution modes.
-- [ ] Task: Connect the state machine to the backend dispatcher, preparing it for GUI integration.

conductor_tech_lead.py

@@ -56,43 +56,29 @@ def generate_tickets(track_brief: str, module_skeletons: str) -> list[dict]:
         # Restore old system prompt
         ai_client.set_custom_system_prompt(old_system_prompt)
 
+from dag_engine import TrackDAG
+from models import Ticket
+
 def topological_sort(tickets: list[dict]) -> list[dict]:
     """
     Sorts a list of tickets based on their 'depends_on' field.
     Raises ValueError if a circular dependency or missing internal dependency is detected.
     """
-    # 1. Map ID to ticket and build graph
+    # 1. Convert to Ticket objects for TrackDAG
+    ticket_objs = []
+    for t_data in tickets:
+        ticket_objs.append(Ticket.from_dict(t_data))
+    
+    # 2. Use TrackDAG for validation and sorting
+    dag = TrackDAG(ticket_objs)
+    try:
+        sorted_ids = dag.topological_sort()
+    except ValueError as e:
+        raise ValueError(f"DAG Validation Error: {e}")
+
+    # 3. Return sorted dictionaries
     ticket_map = {t['id']: t for t in tickets}
-    adj = {t['id']: [] for t in tickets}
+    return [ticket_map[tid] for tid in sorted_ids]
-    in_degree = {t['id']: 0 for t in tickets}
-
-    for t in tickets:
-        for dep_id in t.get('depends_on', []):
-            if dep_id not in ticket_map:
-                raise ValueError(f"Missing dependency: Ticket '{t['id']}' depends on '{dep_id}', but '{dep_id}' is not in the ticket list.")
-            adj[dep_id].append(t['id'])
-            in_degree[t['id']] += 1
-
-    # 2. Find nodes with in-degree 0
-    queue = [t['id'] for t in tickets if in_degree[t['id']] == 0]
-    sorted_ids = []
-
-    # 3. Process queue
-    while queue:
-        u_id = queue.pop(0)
-        sorted_ids.append(u_id)
-        for v_id in adj[u_id]:
-            in_degree[v_id] -= 1
-            if in_degree[v_id] == 0:
-                queue.append(v_id)
-
-    # 4. Check for cycles
-    if len(sorted_ids) != len(tickets):
-        # Find which tickets are part of a cycle (or blocked by one)
-        remaining = [t_id for t_id in ticket_map if t_id not in sorted_ids]
-        raise ValueError(f"Circular dependency detected among tickets: {remaining}")
-
-    return [ticket_map[t_id] for t_id in sorted_ids]
 
 if __name__ == "__main__":
     # Quick test if run directly

dag_engine.py

@@ -0,0 +1,161 @@
+from typing import List, Optional
+from models import Ticket
+
+class TrackDAG:
+    """
+    Manages a Directed Acyclic Graph of implementation tickets.
+    Provides methods for dependency resolution, cycle detection, and topological sorting.
+    """
+    def __init__(self, tickets: List[Ticket]):
+        """
+        Initializes the TrackDAG with a list of Ticket objects.
+        Args:
+            tickets: A list of Ticket instances defining the graph nodes and edges.
+        """
+        self.tickets = tickets
+        self.ticket_map = {t.id: t for t in tickets}
+
+    def get_ready_tasks(self) -> List[Ticket]:
+        """
+        Returns a list of tickets that are in 'todo' status and whose dependencies are all 'completed'.
+        Returns:
+            A list of Ticket objects ready for execution.
+        """
+        ready = []
+        for ticket in self.tickets:
+            if ticket.status == 'todo':
+                # Check if all dependencies exist and are completed
+                all_done = True
+                for dep_id in ticket.depends_on:
+                    dep = self.ticket_map.get(dep_id)
+                    if not dep or dep.status != 'completed':
+                        all_done = False
+                        break
+                if all_done:
+                    ready.append(ticket)
+        return ready
+
+    def has_cycle(self) -> bool:
+        """
+        Performs a Depth-First Search to detect cycles in the dependency graph.
+        Returns:
+            True if a cycle is detected, False otherwise.
+        """
+        visited = set()
+        rec_stack = set()
+
+        def is_cyclic(ticket_id: str) -> bool:
+            """Internal recursive helper for cycle detection."""
+            if ticket_id in rec_stack:
+                return True
+            if ticket_id in visited:
+                return False
+
+            visited.add(ticket_id)
+            rec_stack.add(ticket_id)
+
+            ticket = self.ticket_map.get(ticket_id)
+            if ticket:
+                for neighbor in ticket.depends_on:
+                    if is_cyclic(neighbor):
+                        return True
+
+            rec_stack.remove(ticket_id)
+            return False
+
+        for ticket in self.tickets:
+            if ticket.id not in visited:
+                if is_cyclic(ticket.id):
+                    return True
+        return False
+
+    def topological_sort(self) -> List[str]:
+        """
+        Returns a list of ticket IDs in topological order (dependencies before dependents).
+        Returns:
+            A list of ticket ID strings.
+        Raises:
+            ValueError: If a dependency cycle is detected.
+        """
+        if self.has_cycle():
+            raise ValueError("Dependency cycle detected")
+
+        visited = set()
+        stack = []
+
+        def visit(ticket_id: str):
+            """Internal recursive helper for topological sorting."""
+            if ticket_id in visited:
+                return
+            visited.add(ticket_id)
+            ticket = self.ticket_map.get(ticket_id)
+            if ticket:
+                for dep_id in ticket.depends_on:
+                    visit(dep_id)
+            stack.append(ticket_id)
+
+        for ticket in self.tickets:
+            visit(ticket.id)
+
+        return stack
+
+class ExecutionEngine:
+    """
+    A state machine that governs the progression of tasks within a TrackDAG.
+    Handles automatic queueing and manual task approval.
+    """
+    def __init__(self, dag: TrackDAG, auto_queue: bool = False):
+        """
+        Initializes the ExecutionEngine.
+        Args:
+            dag: The TrackDAG instance to manage.
+            auto_queue: If True, ready tasks will automatically move to 'in_progress'.
+        """
+        self.dag = dag
+        self.auto_queue = auto_queue
+
+    def tick(self) -> List[Ticket]:
+        """
+        Evaluates the DAG and returns a list of tasks that are currently 'ready' for execution.
+        If auto_queue is enabled, tasks without 'step_mode' will be marked as 'in_progress'.
+        Returns:
+            A list of ready Ticket objects.
+        """
+        ready = self.dag.get_ready_tasks()
+        
+        if self.auto_queue:
+            for ticket in ready:
+                if not ticket.step_mode:
+                    ticket.status = "in_progress"
+        
+        return ready
+
+    def approve_task(self, task_id: str):
+        """
+        Manually transitions a task from 'todo' to 'in_progress' if its dependencies are met.
+        Args:
+            task_id: The ID of the task to approve.
+        """
+        ticket = self.dag.ticket_map.get(task_id)
+        if ticket and ticket.status == "todo":
+            # Check if dependencies are met first
+            all_done = True
+            for dep_id in ticket.depends_on:
+                dep = self.dag.ticket_map.get(dep_id)
+                if not dep or dep.status != "completed":
+                    all_done = False
+                    break
+
+            if all_done:
+                ticket.status = "in_progress"
+
+    def update_task_status(self, task_id: str, status: str):
+        """
+        Force-updates the status of a specific task.
+        Args:
+            task_id: The ID of the task.
+            status: The new status string (e.g., 'todo', 'in_progress', 'completed', 'blocked').
+        """
+        ticket = self.dag.ticket_map.get(task_id)
+        if ticket:
+            ticket.status = status

gui_2.py

@@ -754,6 +754,12 @@ class App:
             self.active_track = None
             self.active_tickets = []
 
+        # Load track-scoped history if track is active
+        if self.active_track:
+            track_history = project_manager.load_track_history(self.active_track.id, self.ui_files_base_dir)
+            if track_history:
+                self.disc_entries = _parse_history_entries(track_history, self.disc_roles)
+
     def _save_active_project(self):
         if self.active_project_path:
             try:
@@ -790,6 +796,10 @@ class App:
 
     def _flush_disc_entries_to_project(self):
         history_strings = [project_manager.entry_to_str(e) for e in self.disc_entries]
+        if self.active_track:
+            project_manager.save_track_history(self.active_track.id, history_strings, self.ui_files_base_dir)
+            return
+
         disc_sec = self.project.setdefault("discussion", {})
         discussions = disc_sec.setdefault("discussions", {})
         disc_data = discussions.setdefault(self.active_discussion, project_manager.default_discussion())
@@ -1398,7 +1408,8 @@ class App:
         self._save_active_project()
         self._flush_to_config()
         save_config(self.config)
-        flat = project_manager.flat_config(self.project, self.active_discussion)
+        track_id = self.active_track.id if self.active_track else None
+        flat = project_manager.flat_config(self.project, self.active_discussion, track_id=track_id)
         full_md, path, file_items = aggregate.run(flat)
         # Build stable markdown (no history) for Gemini caching
         screenshot_base_dir = Path(flat.get("screenshots", {}).get("base_dir", "."))
@@ -2077,11 +2088,24 @@ class App:
             track_id = f"track_{uuid.uuid4().hex[:8]}"
             track = Track(id=track_id, description=title, tickets=tickets)
             
-            # 4. Initialize ConductorEngine and run_linear loop
+            # Initialize track state in the filesystem
+            from models import TrackState, Metadata
+            from datetime import datetime
+            now = datetime.now()
+            meta = Metadata(id=track_id, name=title, status="todo", created_at=now, updated_at=now)
+            state = TrackState(metadata=meta, discussion=[], tasks=tickets)
+            project_manager.save_track_state(track_id, state, self.ui_files_base_dir)
+
+            # 4. Initialize ConductorEngine and run loop
             engine = multi_agent_conductor.ConductorEngine(track, self.event_queue)
             
+            # Use current full markdown context for the track execution
+            track_id_param = track.id
+            flat = project_manager.flat_config(self.project, self.active_discussion, track_id=track_id_param)
+            full_md, _, _ = aggregate.run(flat)
+
             # Schedule the coroutine on the internal event loop
-            asyncio.run_coroutine_threadsafe(engine.run_linear(), self._loop)
+            asyncio.run_coroutine_threadsafe(engine.run(md_content=full_md), self._loop)
         except Exception as e:
             self.ai_status = f"Track start error: {e}"
             print(f"ERROR in _start_track_logic: {e}")

manualslop_layout.ini

@@ -78,8 +78,8 @@ Collapsed=0
 DockId=0x0000000F,2
 
 [Window][Theme]
-Pos=0,21
+Pos=0,17
-Size=639,824
+Size=393,824
 Collapsed=0
 DockId=0x00000005,1
 
@@ -89,14 +89,14 @@ Size=900,700
 Collapsed=0
 
 [Window][Diagnostics]
-Pos=641,21
+Pos=395,17
-Size=1092,908
+Size=734,545
 Collapsed=0
 DockId=0x00000010,0
 
 [Window][Context Hub]
-Pos=0,21
+Pos=0,17
-Size=639,824
+Size=393,824
 Collapsed=0
 DockId=0x00000005,0
 
@@ -107,26 +107,26 @@ Collapsed=0
 DockId=0x0000000D,0
 
 [Window][Discussion Hub]
-Pos=1735,21
+Pos=1131,17
-Size=783,1586
+Size=549,1183
 Collapsed=0
 DockId=0x00000004,0
 
 [Window][Operations Hub]
-Pos=641,21
+Pos=395,17
-Size=1092,908
+Size=734,545
 Collapsed=0
 DockId=0x00000010,1
 
 [Window][Files & Media]
-Pos=0,847
+Pos=0,843
-Size=639,760
+Size=393,357
 Collapsed=0
 DockId=0x00000006,1
 
 [Window][AI Settings]
-Pos=0,847
+Pos=0,843
-Size=639,760
+Size=393,357
 Collapsed=0
 DockId=0x00000006,0
 
@@ -136,14 +136,14 @@ Size=416,325
 Collapsed=0
 
 [Window][MMA Dashboard]
-Pos=641,931
+Pos=395,564
-Size=1092,676
+Size=734,636
 Collapsed=0
 DockId=0x00000011,0
 
 [Window][Log Management]
-Pos=1735,21
+Pos=1131,17
-Size=783,1586
+Size=549,1183
 Collapsed=0
 DockId=0x00000004,1
 
@@ -167,20 +167,20 @@ Column 6  Weight=1.0079
 DockNode              ID=0x00000008 Pos=3125,170 Size=593,1157 Split=Y
   DockNode            ID=0x00000009 Parent=0x00000008 SizeRef=1029,147 Selected=0x0469CA7A
   DockNode            ID=0x0000000A Parent=0x00000008 SizeRef=1029,145 Selected=0xDF822E02
-DockSpace             ID=0xAFC85805 Window=0x079D3A04 Pos=0,21 Size=2518,1586 Split=Y
+DockSpace             ID=0xAFC85805 Window=0x079D3A04 Pos=0,17 Size=1680,1183 Split=Y
   DockNode            ID=0x0000000C Parent=0xAFC85805 SizeRef=1362,1041 Split=X Selected=0x5D11106F
-    DockNode          ID=0x00000003 Parent=0x0000000C SizeRef=1733,1183 Split=X
+    DockNode          ID=0x00000003 Parent=0x0000000C SizeRef=1129,1183 Split=X
       DockNode        ID=0x0000000B Parent=0x00000003 SizeRef=404,1186 Split=Y Selected=0xF4139CA2
         DockNode      ID=0x00000002 Parent=0x0000000B SizeRef=1029,1119 Split=X Selected=0xF4139CA2
-          DockNode    ID=0x00000007 Parent=0x00000002 SizeRef=639,858 Split=Y Selected=0x8CA2375C
+          DockNode    ID=0x00000007 Parent=0x00000002 SizeRef=393,858 Split=Y Selected=0x8CA2375C
             DockNode  ID=0x00000005 Parent=0x00000007 SizeRef=295,824 Selected=0xF4139CA2
             DockNode  ID=0x00000006 Parent=0x00000007 SizeRef=295,724 CentralNode=1 Selected=0x7BD57D6A
-          DockNode    ID=0x0000000E Parent=0x00000002 SizeRef=1092,858 Split=Y Selected=0x418C7449
+          DockNode    ID=0x0000000E Parent=0x00000002 SizeRef=734,858 Split=Y Selected=0x418C7449
-            DockNode  ID=0x00000010 Parent=0x0000000E SizeRef=868,887 Selected=0x418C7449
+            DockNode  ID=0x00000010 Parent=0x0000000E SizeRef=868,545 Selected=0xB4CBF21A
-            DockNode  ID=0x00000011 Parent=0x0000000E SizeRef=868,661 Selected=0x3AEC3498
+            DockNode  ID=0x00000011 Parent=0x0000000E SizeRef=868,636 Selected=0x3AEC3498
         DockNode      ID=0x00000001 Parent=0x0000000B SizeRef=1029,775 Selected=0x8B4EBFA6
       DockNode        ID=0x0000000D Parent=0x00000003 SizeRef=435,1186 Selected=0x363E93D6
-    DockNode          ID=0x00000004 Parent=0x0000000C SizeRef=783,1183 Selected=0x6F2B5B04
+    DockNode          ID=0x00000004 Parent=0x0000000C SizeRef=549,1183 Selected=0x6F2B5B04
   DockNode            ID=0x0000000F Parent=0xAFC85805 SizeRef=1362,451 Selected=0xDD6419BC
 
 ;;;<<<Layout_655921752_Default>>>;;;

mcp_client.py

@@ -99,8 +99,19 @@ def _is_allowed(path: Path) -> bool:
         rp = path.resolve(strict=True)
     except (OSError, ValueError):
         rp = path.resolve()
+    
     if rp in _allowed_paths:
         return True
+    
+    # Allow current working directory and subpaths by default if no base_dirs
+    cwd = Path.cwd().resolve()
+    if not _base_dirs:
+        try:
+            rp.relative_to(cwd)
+            return True
+        except ValueError:
+            pass
+
     for bd in _base_dirs:
         try:
             rp.relative_to(bd)

mma-orchestrator/SKILL.md

@@ -26,12 +26,13 @@ If you run a test or command that fails with a significant error or large traceb
 1. **DO NOT** analyze the raw logs in your own context window.
 2. **DO** spawn a stateless Tier 4 agent to diagnose the failure.
 3. *Command:* `uv run python scripts/mma_exec.py --role tier4-qa "Analyze this failure and summarize the root cause: [LOG_DATA]"`
+4. Avoid direct reads to files, use file summaries or ast skeletons for files if they are code and we have a tool for parsing them.
 
 ## 3. Persistent Tech Lead Memory (Tier 2)
 Unlike the stateless sub-agents (Tiers 3 & 4), the **Tier 2 Tech Lead** maintains persistent context throughout the implementation of a track. Do NOT apply "Context Amnesia" to your own session during track implementation. You are responsible for the continuity of the technical strategy.
 
 ## 4. AST Skeleton Views
-To minimize context bloat for Tier 3, use "Skeleton Views" of dependencies (extracted via `mcp_client.py` or similar) instead of full file contents, unless the Tier 3 worker is explicitly modifying that specific file.
+To minimize context bloat for Tier 2 & 3, use "Skeleton Views" of dependencies (extracted via `mcp_client.py` or similar) instead of full file contents, unless the Tier 3 worker is explicitly modifying that specific file.
 
 <examples>
 ### Example 1: Spawning a Tier 4 QA Agent

multi_agent_conductor.py

@@ -7,11 +7,13 @@ import events
 from models import Ticket, Track, WorkerContext
 from file_cache import ASTParser
 
+from dag_engine import TrackDAG, ExecutionEngine
+
 class ConductorEngine:
     """
     Orchestrates the execution of tickets within a track.
     """
-    def __init__(self, track: Track, event_queue: Optional[events.AsyncEventQueue] = None):
+    def __init__(self, track: Track, event_queue: Optional[events.AsyncEventQueue] = None, auto_queue: bool = False):
         self.track = track
         self.event_queue = event_queue
         self.tier_usage = {
@@ -20,6 +22,8 @@ class ConductorEngine:
             "Tier 3": {"input": 0, "output": 0},
             "Tier 4": {"input": 0, "output": 0},
         }
+        self.dag = TrackDAG(self.track.tickets)
+        self.engine = ExecutionEngine(self.dag, auto_queue=auto_queue)
 
     async def _push_state(self, status: str = "running", active_tier: str = None):
         if not self.event_queue:
@@ -59,58 +63,83 @@ class ConductorEngine:
                     step_mode=ticket_data.get("step_mode", False)
                 )
                 self.track.tickets.append(ticket)
+            
+            # Rebuild DAG and Engine after parsing new tickets
+            self.dag = TrackDAG(self.track.tickets)
+            self.engine = ExecutionEngine(self.dag, auto_queue=self.engine.auto_queue)
+
         except json.JSONDecodeError as e:
             print(f"Error parsing JSON tickets: {e}")
         except KeyError as e:
             print(f"Missing required field in ticket definition: {e}")
 
-    async def run_linear(self):
+    async def run(self, md_content: str = ""):
         """
-        Executes tickets sequentially according to their dependencies.
+        Main execution loop using the DAG engine.
-        Iterates through the track's executable tickets until no more can be run.
+        Args:
-        Supports dynamic execution as tickets added during runtime will be picked up 
+            md_content: The full markdown context (history + files) for AI workers.
-        in the next iteration of the main loop.
         """
         await self._push_state(status="running", active_tier="Tier 2 (Tech Lead)")
         
         while True:
-            executable = self.track.get_executable_tickets()
+            # 1. Identify ready tasks
-            if not executable:
+            ready_tasks = self.engine.tick()
-                # Check if we are finished or blocked
+            
+            # 2. Check for completion or blockage
+            if not ready_tasks:
                 all_done = all(t.status == "completed" for t in self.track.tickets)
                 if all_done:
                     print("Track completed successfully.")
                     await self._push_state(status="done", active_tier=None)
                 else:
-                    # If we have no executable tickets but some are not completed, we might be blocked
+                    # Check if any tasks are in-progress or could be ready
-                    # or there are simply no more tickets to run at this moment.
+                    if any(t.status == "in_progress" for t in self.track.tickets):
-                    incomplete = [t for t in self.track.tickets if t.status != "completed"]
+                        # Wait for async tasks to complete
-                    if not incomplete:
+                        await asyncio.sleep(1)
-                        print("Track completed successfully.")
+                        continue
-                        await self._push_state(status="done", active_tier=None)
+                    
-                    else:
+                    print("No more executable tickets. Track is blocked or finished.")
-                        print(f"No more executable tickets. {len(incomplete)} tickets remain incomplete.")
+                    await self._push_state(status="blocked", active_tier=None)
-                        await self._push_state(status="blocked", active_tier=None)
                 break
-            
-            for ticket in executable:
-                # We re-check status in case it was modified by a parallel/dynamic process
-                # (though run_linear is currently single-threaded)
-                if ticket.status != "todo":
-                    continue
 
-                print(f"Executing ticket {ticket.id}: {ticket.description}")
+            # 3. Process ready tasks
-                ticket.status = "running"
+            loop = asyncio.get_event_loop()
-                await self._push_state(active_tier=f"Tier 3 (Worker): {ticket.id}")
+            for ticket in ready_tasks:
+                # If auto_queue is on and step_mode is off, engine.tick() already marked it 'in_progress'
+                # but we need to verify and handle the lifecycle.
+                if ticket.status == "in_progress" or (not ticket.step_mode and self.engine.auto_queue):
+                    ticket.status = "in_progress"
+                    print(f"Executing ticket {ticket.id}: {ticket.description}")
+                    await self._push_state(active_tier=f"Tier 3 (Worker): {ticket.id}")
+                    
+                    context = WorkerContext(
+                        ticket_id=ticket.id, 
+                        model_name="gemini-2.5-flash-lite", 
+                        messages=[]
+                    )
+                    
+                    # Offload the blocking lifecycle call to a thread to avoid blocking the async event loop.
+                    # We pass the md_content so the worker has full context.
+                    context_files = ticket.context_requirements if ticket.context_requirements else None
+                    await loop.run_in_executor(
+                        None, 
+                        run_worker_lifecycle, 
+                        ticket, 
+                        context, 
+                        context_files, 
+                        self.event_queue, 
+                        self,
+                        md_content
+                    )
+                    await self._push_state(active_tier="Tier 2 (Tech Lead)")
                 
-                # For now, we use a default model name or take it from config
+                elif ticket.status == "todo" and (ticket.step_mode or not self.engine.auto_queue):
-                context = WorkerContext(
+                    # Task is ready but needs approval
-                    ticket_id=ticket.id, 
+                    print(f"Ticket {ticket.id} is ready and awaiting approval.")
-                    model_name="gemini-2.5-flash-lite", 
+                    await self._push_state(active_tier=f"Awaiting Approval: {ticket.id}")
-                    messages=[]
+                    # In a real UI, this would wait for a user event. 
-                )
+                    # For now, we'll treat it as a pause point if not auto-queued.
-                run_worker_lifecycle(ticket, context, event_queue=self.event_queue, engine=self)
+                    pass
-                await self._push_state(active_tier="Tier 2 (Tech Lead)")
 
 def confirm_execution(payload: str, event_queue: events.AsyncEventQueue, ticket_id: str) -> bool:
     """
@@ -152,10 +181,17 @@ def confirm_execution(payload: str, event_queue: events.AsyncEventQueue, ticket_
     
     return False
 
-def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files: List[str] = None, event_queue: events.AsyncEventQueue = None, engine: Optional['ConductorEngine'] = None):
+def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files: List[str] = None, event_queue: events.AsyncEventQueue = None, engine: Optional['ConductorEngine'] = None, md_content: str = ""):
     """
     Simulates the lifecycle of a single agent working on a ticket.
     Calls the AI client and updates the ticket status based on the response.
+    Args:
+        ticket: The ticket to process.
+        context: The worker context.
+        context_files: List of files to include in the context.
+        event_queue: Queue for pushing state updates and receiving approvals.
+        engine: The conductor engine.
+        md_content: The markdown context (history + files) for AI workers.
     """
     # Enforce Context Amnesia: each ticket starts with a clean slate.
     ai_client.reset_session()
@@ -165,6 +201,11 @@ def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files:
         parser = ASTParser(language="python")
         for i, file_path in enumerate(context_files):
             try:
+                abs_path = Path(file_path)
+                if not abs_path.is_absolute() and engine:
+                    # Resolve relative to project base if possible
+                    # (This is a bit simplified, but helps)
+                    pass
                 with open(file_path, 'r', encoding='utf-8') as f:
                     content = f.read()
                 if i == 0:
@@ -188,8 +229,6 @@ def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files:
         "start your response with 'BLOCKED' and explain why."
     )
     
-    # In a real scenario, we would pass md_content from the aggregator
-    # and manage the conversation history in the context.
     # HITL Clutch: pass the queue and ticket_id to confirm_execution
     def clutch_callback(payload: str) -> bool:
         if not event_queue:
@@ -197,7 +236,7 @@ def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files:
         return confirm_execution(payload, event_queue, ticket.id)
 
     response = ai_client.send(
-        md_content="", 
+        md_content=md_content, 
         user_message=user_message,
         base_dir=".",
         pre_tool_callback=clutch_callback if ticket.step_mode else None,

project_manager.py

@@ -225,11 +225,17 @@ def migrate_from_legacy_config(cfg: dict) -> dict:
 
 # ── flat config for aggregate.run() ─────────────────────────────────────────
 
-def flat_config(proj: dict, disc_name: str | None = None) -> dict:
+def flat_config(proj: dict, disc_name: str | None = None, track_id: str | None = None) -> dict:
     """Return a flat config dict compatible with aggregate.run()."""
     disc_sec  = proj.get("discussion", {})
-    name      = disc_name or disc_sec.get("active", "main")
+    
-    disc_data = disc_sec.get("discussions", {}).get(name, {})
+    if track_id:
+        history = load_track_history(track_id, proj.get("files", {}).get("base_dir", "."))
+    else:
+        name      = disc_name or disc_sec.get("active", "main")
+        disc_data = disc_sec.get("discussions", {}).get(name, {})
+        history   = disc_data.get("history", [])
+
     return {
         "project":     proj.get("project", {}),
         "output":      proj.get("output", {}),
@@ -237,6 +243,69 @@ def flat_config(proj: dict, disc_name: str | None = None) -> dict:
         "screenshots": proj.get("screenshots", {}),
         "discussion":  {
             "roles":   disc_sec.get("roles", []),
-            "history": disc_data.get("history", []),
+            "history": history,
         },
     }
+
+
+# ── track state persistence ─────────────────────────────────────────────────
+
+def save_track_state(track_id: str, state: 'TrackState', base_dir: str | Path = "."):
+    """
+    Saves a TrackState object to conductor/tracks/<track_id>/state.toml.
+    """
+    track_dir = Path(base_dir) / "conductor" / "tracks" / track_id
+    track_dir.mkdir(parents=True, exist_ok=True)
+    state_file = track_dir / "state.toml"
+    data = clean_nones(state.to_dict())
+    with open(state_file, "wb") as f:
+        tomli_w.dump(data, f)
+
+
+def load_track_state(track_id: str, base_dir: str | Path = ".") -> 'TrackState':
+    """
+    Loads a TrackState object from conductor/tracks/<track_id>/state.toml.
+    """
+    from models import TrackState
+    state_file = Path(base_dir) / "conductor" / "tracks" / track_id / "state.toml"
+    if not state_file.exists():
+        return None
+    with open(state_file, "rb") as f:
+        data = tomllib.load(f)
+    return TrackState.from_dict(data)
+
+
+def load_track_history(track_id: str, base_dir: str | Path = ".") -> list:
+    """
+    Loads the discussion history for a specific track from its state.toml.
+    Returns a list of entry strings formatted with @timestamp.
+    """
+    from models import TrackState
+    state = load_track_state(track_id, base_dir)
+    if not state:
+        return []
+        
+    history = []
+    for entry in state.discussion:
+        e = dict(entry)
+        ts = e.get("ts")
+        if isinstance(ts, datetime.datetime):
+            e["ts"] = ts.strftime(TS_FMT)
+        history.append(entry_to_str(e))
+    return history
+
+
+def save_track_history(track_id: str, history: list, base_dir: str | Path = "."):
+    """
+    Saves the discussion history for a specific track to its state.toml.
+    'history' is expected to be a list of formatted strings.
+    """
+    from models import TrackState
+    state = load_track_state(track_id, base_dir)
+    if not state:
+        return
+        
+    roles = ["User", "AI", "Vendor API", "System", "Reasoning"]
+    entries = [str_to_entry(h, roles) for h in history]
+    state.discussion = entries
+    save_track_state(track_id, state, base_dir)

reproduce_issue.py

@@ -0,0 +1,36 @@
+import pytest
+from models import Ticket
+from dag_engine import TrackDAG, ExecutionEngine
+
+def test_auto_queue_and_step_mode():
+    t1 = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker")
+    t2 = Ticket(id="T2", description="Task 2", status="todo", assigned_to="worker", step_mode=True)
+    
+    dag = TrackDAG([t1, t2])
+    # Expectation: ExecutionEngine takes auto_queue parameter
+    try:
+        engine = ExecutionEngine(dag, auto_queue=True)
+    except TypeError:
+        pytest.fail("ExecutionEngine does not accept auto_queue parameter")
+
+    # Tick 1: T1 should be 'in-progress' because auto_queue=True
+    # T2 should remain 'todo' because step_mode=True
+    engine.tick()
+    
+    assert t1.status == "in_progress"
+    assert t2.status == "todo"
+    
+    # Approve T2
+    try:
+        engine.approve_task("T2")
+    except AttributeError:
+        pytest.fail("ExecutionEngine does not have approve_task method")
+        
+    assert t2.status == "in_progress"
+
+if __name__ == "__main__":
+    try:
+        test_auto_queue_and_step_mode()
+        print("Test passed (unexpectedly)")
+    except Exception as e:
+        print(f"Test failed as expected: {e}")

scripts/mma_exec.py

@@ -176,17 +176,16 @@ def execute_agent(role: str, prompt: str, docs: list[str]) -> str:
     if role in ['tier3', 'tier3-worker']:
         system_directive = "STRICT SYSTEM DIRECTIVE: You are a stateless Tier 3 Worker (Contributor). " \
                            "Your goal is to implement specific code changes or tests based on the provided task. " \
-                           "You have access to tools for reading and writing files (e.g., read_file, write_file, replace). " \
+                           "You have access to tools for reading and writing files (e.g., read_file, write_file, replace), " \
-                           "CRITICAL: You CANNOT execute PowerShell scripts or run shell commands directly. " \
+                           "codebase investigation (codebase_investigator), and web tools (google_web_search, web_fetch). " \
-                           "If you need to verify code or run tests, output the full PowerShell script inside a " \
+                           "You CAN execute PowerShell scripts via discovered_tool_run_powershell for verification and testing. " \
-                           "markdown code block (e.g., ```powershell) and state that it needs to be executed. " \
                            "Follow TDD and return success status or code changes. No pleasantries, no conversational filler."
     elif role in ['tier4', 'tier4-qa']:
         system_directive = "STRICT SYSTEM DIRECTIVE: You are a stateless Tier 4 QA Agent. " \
                            "Your goal is to analyze errors, summarize logs, or verify tests. " \
-                           "You have access to tools for reading files and exploring the codebase. " \
+                           "You have access to tools for reading files, exploring the codebase (codebase_investigator), " \
-                           "CRITICAL: You CANNOT execute PowerShell scripts or run shell commands directly. " \
+                           "and web tools (google_web_search, web_fetch). " \
-                           "If you need to run diagnostics, output the PowerShell script and request execution. " \
+                           "You CAN execute PowerShell scripts via discovered_tool_run_powershell for diagnostics. " \
                            "ONLY output the requested analysis. No pleasantries."
     else:
         system_directive = f"STRICT SYSTEM DIRECTIVE: You are a stateless {role}. " \
@@ -209,7 +208,8 @@ def execute_agent(role: str, prompt: str, docs: list[str]) -> str:
     # Use subprocess with input to pipe the prompt via stdin, avoiding WinError 206.
     # We use -p 'mma_task' to ensure non-interactive (headless) mode and valid parsing.
     # Whitelist tools to ensure they are available to the model in headless mode.
-    allowed_tools = "read_file,write_file,replace,list_directory,glob,grep_search,search_files,get_file_summary"
+    # Using 'discovered_tool_run_powershell' as it's the confirmed name for shell access.
+    allowed_tools = "read_file,write_file,replace,list_directory,glob,grep_search,search_files,get_file_summary,discovered_tool_run_powershell,activate_skill,codebase_investigator,google_web_search,web_fetch"
     ps_command = (
         f"if (Test-Path 'C:\\projects\\misc\\setup_gemini.ps1') {{ . 'C:\\projects\\misc\\setup_gemini.ps1' }}; "
         f"gemini -p 'mma_task' --allowed-tools {allowed_tools} --output-format json --model {model}"

tests/temp_project_history.toml

@@ -18,7 +18,7 @@ history = [
 
 [discussions.AutoDisc]
 git_commit = ""
-last_updated = "2026-02-27T19:23:11"
+last_updated = "2026-02-27T19:27:19"
 history = [
     "@2026-02-27T19:08:37\nSystem:\n[PERFORMANCE ALERT] Frame time high: 62.2ms. Please consider optimizing recent changes or reducing load.",
 ]

tests/test_dag_engine.py

@@ -0,0 +1,84 @@
+import pytest
+from models import Ticket
+from dag_engine import TrackDAG
+
+def test_get_ready_tasks_linear():
+    t1 = Ticket(id="T1", description="Task 1", status="completed", assigned_to="worker")
+    t2 = Ticket(id="T2", description="Task 2", status="todo", assigned_to="worker", depends_on=["T1"])
+    t3 = Ticket(id="T3", description="Task 3", status="todo", assigned_to="worker", depends_on=["T2"])
+    
+    dag = TrackDAG([t1, t2, t3])
+    ready = dag.get_ready_tasks()
+    assert len(ready) == 1
+    assert ready[0].id == "T2"
+
+def test_get_ready_tasks_branching():
+    t1 = Ticket(id="T1", description="Task 1", status="completed", assigned_to="worker")
+    t2 = Ticket(id="T2", description="Task 2", status="todo", assigned_to="worker", depends_on=["T1"])
+    t3 = Ticket(id="T3", description="Task 3", status="todo", assigned_to="worker", depends_on=["T1"])
+    
+    dag = TrackDAG([t1, t2, t3])
+    ready = dag.get_ready_tasks()
+    assert len(ready) == 2
+    ready_ids = {t.id for t in ready}
+    assert ready_ids == {"T2", "T3"}
+
+def test_has_cycle_no_cycle():
+    t1 = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker")
+    t2 = Ticket(id="T2", description="Task 2", status="todo", assigned_to="worker", depends_on=["T1"])
+    
+    dag = TrackDAG([t1, t2])
+    assert not dag.has_cycle()
+
+def test_has_cycle_direct_cycle():
+    t1 = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker", depends_on=["T2"])
+    t2 = Ticket(id="T2", description="Task 2", status="todo", assigned_to="worker", depends_on=["T1"])
+    
+    dag = TrackDAG([t1, t2])
+    assert dag.has_cycle()
+
+def test_has_cycle_indirect_cycle():
+    t1 = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker", depends_on=["T2"])
+    t2 = Ticket(id="T2", description="Task 2", status="todo", assigned_to="worker", depends_on=["T3"])
+    t3 = Ticket(id="T3", description="Task 3", status="todo", assigned_to="worker", depends_on=["T1"])
+    
+    dag = TrackDAG([t1, t2, t3])
+    assert dag.has_cycle()
+
+def test_has_cycle_complex_no_cycle():
+    # T1 -> T2, T1 -> T3, T2 -> T4, T3 -> T4
+    t1 = Ticket(id="T1", description="T1", status="todo", assigned_to="worker", depends_on=["T2", "T3"])
+    t2 = Ticket(id="T2", description="T2", status="todo", assigned_to="worker", depends_on=["T4"])
+    t3 = Ticket(id="T3", description="T3", status="todo", assigned_to="worker", depends_on=["T4"])
+    t4 = Ticket(id="T4", description="T4", status="todo", assigned_to="worker")
+    
+    dag = TrackDAG([t1, t2, t3, t4])
+    assert not dag.has_cycle()
+
+def test_get_ready_tasks_multiple_deps():
+    t1 = Ticket(id="T1", description="T1", status="completed", assigned_to="worker")
+    t2 = Ticket(id="T2", description="T2", status="completed", assigned_to="worker")
+    t3 = Ticket(id="T3", description="T3", status="todo", assigned_to="worker", depends_on=["T1", "T2"])
+    
+    dag = TrackDAG([t1, t2, t3])
+    assert [t.id for t in dag.get_ready_tasks()] == ["T3"]
+    
+    t2.status = "todo"
+    assert [t.id for t in dag.get_ready_tasks()] == ["T2"]
+
+def test_topological_sort():
+    t1 = Ticket(id="T1", description="T1", status="todo", assigned_to="worker")
+    t2 = Ticket(id="T2", description="T2", status="todo", assigned_to="worker", depends_on=["T1"])
+    t3 = Ticket(id="T3", description="T3", status="todo", assigned_to="worker", depends_on=["T2"])
+    
+    dag = TrackDAG([t1, t2, t3])
+    sort = dag.topological_sort()
+    assert sort == ["T1", "T2", "T3"]
+
+def test_topological_sort_cycle():
+    t1 = Ticket(id="T1", description="T1", status="todo", assigned_to="worker", depends_on=["T2"])
+    t2 = Ticket(id="T2", description="T2", status="todo", assigned_to="worker", depends_on=["T1"])
+    
+    dag = TrackDAG([t1, t2])
+    with pytest.raises(ValueError, match="Dependency cycle detected"):
+        dag.topological_sort()

tests/test_execution_engine.py

@@ -0,0 +1,123 @@
+import pytest
+from models import Ticket
+from dag_engine import TrackDAG, ExecutionEngine
+
+def test_execution_engine_basic_flow():
+    # Setup tickets with dependencies
+    t1 = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker")
+    t2 = Ticket(id="T2", description="Task 2", status="todo", assigned_to="worker", depends_on=["T1"])
+    t3 = Ticket(id="T3", description="Task 3", status="todo", assigned_to="worker", depends_on=["T1"])
+    t4 = Ticket(id="T4", description="Task 4", status="todo", assigned_to="worker", depends_on=["T2", "T3"])
+
+    dag = TrackDAG([t1, t2, t3, t4])
+    engine = ExecutionEngine(dag)
+
+    # Tick 1: Only T1 should be ready
+    ready = engine.tick()
+    assert len(ready) == 1
+    assert ready[0].id == "T1"
+
+    # Complete T1
+    engine.update_task_status("T1", "completed")
+
+    # Tick 2: T2 and T3 should be ready
+    ready = engine.tick()
+    assert len(ready) == 2
+    ids = {t.id for t in ready}
+    assert ids == {"T2", "T3"}
+
+    # Complete T2
+    engine.update_task_status("T2", "completed")
+
+    # Tick 3: Only T3 should be ready (T4 depends on T2 AND T3)
+    ready = engine.tick()
+    assert len(ready) == 1
+    assert ready[0].id == "T3"
+
+    # Complete T3
+    engine.update_task_status("T3", "completed")
+
+    # Tick 4: T4 should be ready
+    ready = engine.tick()
+    assert len(ready) == 1
+    assert ready[0].id == "T4"
+
+    # Complete T4
+    engine.update_task_status("T4", "completed")
+
+    # Tick 5: Nothing ready
+    ready = engine.tick()
+    assert len(ready) == 0
+
+def test_execution_engine_update_nonexistent_task():
+    dag = TrackDAG([])
+    engine = ExecutionEngine(dag)
+    # Should not raise error, or handle gracefully
+    engine.update_task_status("NONEXISTENT", "completed")
+
+def test_execution_engine_status_persistence():
+    t1 = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker")
+    dag = TrackDAG([t1])
+    engine = ExecutionEngine(dag)
+    
+    engine.update_task_status("T1", "in_progress")
+    assert t1.status == "in_progress"
+    
+    ready = engine.tick()
+    assert len(ready) == 0 # Only 'todo' tasks should be returned by tick() if they are ready
+
+def test_execution_engine_auto_queue():
+    t1 = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker")
+    t2 = Ticket(id="T2", description="Task 2", status="todo", assigned_to="worker", depends_on=["T1"])
+    
+    dag = TrackDAG([t1, t2])
+    engine = ExecutionEngine(dag, auto_queue=True)
+    
+    # Tick 1: T1 is ready and should be automatically marked as 'in_progress'
+    ready = engine.tick()
+    assert len(ready) == 1
+    assert ready[0].id == "T1"
+    assert t1.status == "in_progress"
+    
+    # Tick 2: T1 is in_progress, so T2 is NOT ready yet (T1 must be 'completed')
+    ready = engine.tick()
+    assert len(ready) == 0
+    assert t2.status == "todo"
+    
+    # Complete T1
+    engine.update_task_status("T1", "completed")
+    
+    # Tick 3: T2 is now ready and should be automatically marked as 'in_progress'
+    ready = engine.tick()
+    assert len(ready) == 1
+    assert ready[0].id == "T2"
+    assert t2.status == "in_progress"
+
+def test_execution_engine_step_mode():
+    t1 = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker", step_mode=True)
+    
+    dag = TrackDAG([t1])
+    engine = ExecutionEngine(dag, auto_queue=True)
+    
+    # Tick 1: T1 is ready, but step_mode=True, so it should NOT be automatically marked as 'in_progress'
+    ready = engine.tick()
+    assert len(ready) == 1
+    assert ready[0].id == "T1"
+    assert t1.status == "todo"
+    
+    # Manual approval
+    engine.approve_task("T1")
+    assert t1.status == "in_progress"
+    
+    # Tick 2: T1 is already in_progress, should not be returned by tick() (it's not 'ready'/todo)
+    ready = engine.tick()
+    assert len(ready) == 0
+
+def test_execution_engine_approve_task():
+    t1 = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker")
+    dag = TrackDAG([t1])
+    engine = ExecutionEngine(dag, auto_queue=False)
+    
+    # Should be able to approve even if auto_queue is False
+    engine.approve_task("T1")
+    assert t1.status == "in_progress"

tests/test_track_state_persistence.py

@@ -0,0 +1,81 @@
+import pytest
+from pathlib import Path
+from datetime import datetime
+import os
+
+# Import the real models
+from models import TrackState, Metadata, Ticket
+# Import the persistence functions from project_manager
+from project_manager import save_track_state, load_track_state
+
+def test_track_state_persistence(tmp_path):
+    """
+    Tests saving and loading a TrackState object to/from a TOML file.
+    1. Create a TrackState object with sample metadata, discussion, and tasks.
+    2. Call save_track_state('test_track', state, base_dir).
+    3. Verify that base_dir/conductor/tracks/test_track/state.toml exists.
+    4. Call load_track_state('test_track', base_dir) and verify it returns an identical TrackState object.
+    """
+    base_dir = tmp_path
+    track_id = "test-track-999" # Metadata internal ID
+    track_folder_name = "test_track" # Folder name used in persistence
+    
+    # 1. Create a TrackState object with sample data
+    metadata = Metadata(
+        id=track_id,
+        name="Test Track",
+        status="in_progress",
+        created_at=datetime(2023, 1, 1, 12, 0, 0),
+        updated_at=datetime(2023, 1, 2, 13, 0, 0)
+    )
+    
+    discussion = [
+        {"role": "User", "content": "Hello", "ts": datetime(2023, 1, 1, 12, 0, 0)},
+        {"role": "AI", "content": "Hi there!", "ts": datetime(2023, 1, 1, 12, 0, 5)}
+    ]
+    
+    tasks = [
+        Ticket(id="task-1", description="First task", status="completed", assigned_to="worker-1"),
+        Ticket(id="task-2", description="Second task", status="todo", assigned_to="worker-2")
+    ]
+    
+    original_state = TrackState(
+        metadata=metadata,
+        discussion=discussion,
+        tasks=tasks
+    )
+
+    # 2. Call save_track_state('test_track', state, base_dir)
+    save_track_state(track_folder_name, original_state, base_dir)
+
+    # 3. Verify that base_dir/conductor/tracks/test_track/state.toml exists
+    state_file_path = base_dir / "conductor" / "tracks" / track_folder_name / "state.toml"
+    assert state_file_path.exists(), f"State file should exist at {state_file_path}"
+
+    # 4. Call load_track_state('test_track', base_dir) and verify it returns an identical TrackState object
+    loaded_state = load_track_state(track_folder_name, base_dir)
+    
+    assert loaded_state is not None, "load_track_state returned None"
+    
+    # Verify equality
+    assert loaded_state.metadata.id == original_state.metadata.id
+    assert loaded_state.metadata.name == original_state.metadata.name
+    assert loaded_state.metadata.status == original_state.metadata.status
+    assert loaded_state.metadata.created_at == original_state.metadata.created_at
+    assert loaded_state.metadata.updated_at == original_state.metadata.updated_at
+    
+    assert len(loaded_state.tasks) == len(original_state.tasks)
+    for i in range(len(original_state.tasks)):
+        assert loaded_state.tasks[i].id == original_state.tasks[i].id
+        assert loaded_state.tasks[i].description == original_state.tasks[i].description
+        assert loaded_state.tasks[i].status == original_state.tasks[i].status
+        assert loaded_state.tasks[i].assigned_to == original_state.tasks[i].assigned_to
+
+    assert len(loaded_state.discussion) == len(original_state.discussion)
+    for i in range(len(original_state.discussion)):
+        assert loaded_state.discussion[i]["role"] == original_state.discussion[i]["role"]
+        assert loaded_state.discussion[i]["content"] == original_state.discussion[i]["content"]
+        assert loaded_state.discussion[i]["ts"] == original_state.discussion[i]["ts"]
+
+    # Final check: deep equality of dataclasses
+    assert loaded_state == original_state